Accurate localisation is important as a fundamental driver to facilitate many location-aware applications in different scenarios, from human or item tracking in commercial premises, drone tracking to industrial/domestic smart city/home, indoor robot and Internet of Things (IoT) navigation management. Localisation is one of the key technologies in autonomous mobile robot navigation and IoT-related applications where it is fundamental to route planning and avoidance obstacle of mobile IoTs. Indoor localisation is challenging.
Existing localisation technologies can be classified based on their data processing methods. Time of Arrival (ToA), Angle of Arrival (AoA) and Received Signal Strength (RSS) methods are known. Among these, AoA based technology is commonly employed. Such technology is, however, characterised by high computation complexity and susceptibility to erroneous localisation of mobile targets. The Multiple Signal Classification (MUSIC) algorithm in particular uses high computation complexity eigen decomposition operation intensively. In addition, hardware for performing known AoA methods can be costly. AoA estimation performance may also degrade with long processing time where the target has moved significantly.